Modelling Customer Churn using NGBoost

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References

Load the libraries

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Colab

Useful Scripts

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Load the Data

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Data Processing

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Data Processing

Data Types

Train and Test Data

Numerical and Categorical Features

Custom Features

One hot encoding

Train Validation Split

Modelling

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Dist             = distribution eg. Bernoulli,
Score            = score eg. LogScore,
Base             = eg. DecisionTreeRegressor()
natural_gradient = True,
n_estimators     = 500,
learning_rate    = 0.01,
minibatch_frac   = 1.0,
col_sample       = 1.0,
verbose          = True,
verbose_eval     = 100,
tol              = 0.0001,
random_state     = None,
#=================================
NGBClassifier.fit(X,Y,
X_val                 = None,
Y_val                 = None,
sample_weight         = None,
val_sample_weight     = None,
train_loss_monitor    = None,
val_loss_monitor      = None,
early_stopping_rounds = None,
)

Setup Logging

NGBoost HPO Using Hyperopt

Model Evaluation

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Time Taken

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